0.1 Libraries
library(tidyverse)
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## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.3 ✓ dplyr 1.0.2
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0
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library(here)
## Warning: package 'here' was built under R version 4.0.2
## here() starts at /Users/keegan.korthauer/Dropbox/Work/Teaching/Datajam2020/project_8
1.0 Read in Data
gcsc_dat <- read_csv(here("cleaned_gcsc_data.csv"))
## Parsed with column specification:
## cols(
## .default = col_double(),
## site_name = col_character(),
## province = col_character(),
## nearest_city = col_character(),
## municipality = col_character(),
## group_name_2017_onwards = col_character(),
## group_type = col_character(),
## site_shoreline_type = col_character(),
## cleanup_date = col_datetime(format = ""),
## date = col_date(format = ""),
## time = col_time(format = ""),
## unusual_items = col_character(),
## `Site Name` = col_character()
## )
## See spec(...) for full column specifications.
ban_dat <- read_csv(here("ban_data.csv"))
## Parsed with column specification:
## cols(
## Type = col_character(),
## City = col_character(),
## Province = col_character(),
## `Effective Date` = col_character()
## )
2.0 Summary Plots
2.1 Types of plastic found by geographical location
plastics <- gcsc_dat %>%
pivot_longer(names_to = "plastic_type",
values_to = "number_collected",
cols = c(22:27, 30, 32:34, 37:39,42, 46 )) %>%
mutate(plastic_type_new = ifelse(str_detect(plastic_type, "bottles"), "bottles", plastic_type))
plastics_group <-plastics %>%
group_by(year, province, plastic_type_new) %>%
summarise(total = sum(number_collected, na.rm = T))
## `summarise()` regrouping output by 'year', 'province' (override with `.groups` argument)
bar_plot_type <- ggplot(plastics_group,
aes(x=province,
y=total,
group=plastic_type_new,
fill = plastic_type_new)) +
scale_y_log10()+
geom_col() +
labs(title="Amount of plastics by type and province")
bar_plot_type
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 86 rows containing missing values (geom_col).
ggsave(here("plastics_by_type_bar.png"),width = 10, height = 6)
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 86 rows containing missing values (geom_col).
plastics_group2 <-plastics %>%
group_by(year, province, plastic_type_new) %>%
summarise(total_kg = sum(kilograms,na.rm=T),
item_kg=sum(number_collected, na.rm = T)/total_kg) %>%
mutate(year_lab = as.character(year))
## `summarise()` regrouping output by 'year', 'province' (override with `.groups` argument)
plastics_group2
## # A tibble: 560 x 6
## # Groups: year, province [40]
## year province plastic_type_new total_kg item_kg year_lab
## <dbl> <chr> <chr> <dbl> <dbl> <chr>
## 1 2017 AB bags_plastic_plastic_bags_from_2017… 7793 0.210 2017
## 2 2017 AB bottle_caps_plastic_and_metal_2017_… 7793 0.391 2017
## 3 2017 AB bottles 15586 0.0660 2017
## 4 2017 AB buoys_floats_fishing_buoy_pot_or_tr… 7793 0.00385 2017
## 5 2017 AB cigarettes_cigarette_filters 7793 3.17 2017
## 6 2017 AB coffee_cups 7793 0.0802 2017
## 7 2017 AB cups_and_plates_plastic_plastic_cup… 7793 0.0671 2017
## 8 2017 AB food_wrappers_containers 7793 0.489 2017
## 9 2017 AB forks_knives_spoons_utensils_from_2… 7793 0.0595 2017
## 10 2017 AB other_plastic_foam_packaging_other_… 7793 0.213 2017
## # … with 550 more rows
bar_plot_type2 <- ggplot(plastics_group2,
aes(x=province,
y=total_kg )) +
scale_y_log10()+
geom_col() +
labs(title="Kg of plastics by province")
bar_plot_type2
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 28 rows containing missing values (geom_col).
ggsave(here("plastics_bar_kg.png"),width = 10, height = 6)
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 28 rows containing missing values (geom_col).
bar_plot_type2 <- ggplot(plastics_group2,
aes(x=province,
y=item_kg,
group=plastic_type_new,
fill = plastic_type_new)) +
geom_col() +
labs(title="Type of plastic by kg collected and province")
bar_plot_type2
## Warning: Removed 28 rows containing missing values (position_stack).
ggsave(here("kg_plastics_by_type_bar.png"),width = 10, height = 6)
## Warning: Removed 28 rows containing missing values (position_stack).
2.2 Time plots (Roshni)
total_plastics_time <- plastics %>%
group_by(year, province) %>%
summarise(total = sum(number_collected, na.rm = T)) %>%
mutate(year_lab = as.character(year))
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
ggplot(total_plastics_time,
aes(x=year_lab,
y=total,
group=province,
colour=province)) +
# facet_wrap(province ~.) +
geom_line()+
scale_y_log10()
## Warning: Transformation introduced infinite values in continuous y-axis
# scale_colour_manual(values=myCol)
ggsave(here("total_plastics_time.jpeg"), width = 10, height = 6 )
## Warning: Transformation introduced infinite values in continuous y-axis
kg_plastics_time <- plastics %>%
group_by(year, province) %>%
summarise(total_kg = sum(kilograms, na.rm = T)) %>%
mutate(year_lab = as.character(year))
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
ggplot(kg_plastics_time,
aes(x=year_lab,
y=total_kg,
group=province,
colour=province)) +
geom_line()+
scale_y_log10()
## Warning: Transformation introduced infinite values in continuous y-axis
kg_plastics_time
## # A tibble: 40 x 4
## # Groups: year [3]
## year province total_kg year_lab
## <dbl> <chr> <dbl> <chr>
## 1 2017 AB 116895 2017
## 2 2017 BC 603615 2017
## 3 2017 MB 13560 2017
## 4 2017 NB 35610 2017
## 5 2017 NL 14400 2017
## 6 2017 NS 38925 2017
## 7 2017 NT 735 2017
## 8 2017 NU 37500 2017
## 9 2017 ON 368115 2017
## 10 2017 PE 23685 2017
## # … with 30 more rows
ggsave(here("kg_plastics_time.jpeg"), width = 10, height = 6 )
## Warning: Transformation introduced infinite values in continuous y-axis
####Grouping by year & Province & type
plastics_group <-plastics %>%
group_by(year, province, plastic_type_new) %>%
summarise(total = sum(number_collected, na.rm = T)) %>%
mutate(year_lab = as.character(year))
## `summarise()` regrouping output by 'year', 'province' (override with `.groups` argument)
prov_names <- unique(plastics_group$province)
for (i in seq_along(prov_names)) {
chart <- subset(plastics_group, plastics_group$province == prov_names[i])
plot <- ggplot(chart,
aes(x=year_lab,
y=total,
group=plastic_type_new,
colour=plastic_type_new)) +
geom_line()+
theme(legend.position = "right") +
labs(title = paste("Total items by Type of Plastic for", prov_names[i]))+
scale_y_log10()
print(plot)
ggsave( here(paste("Total items by Type of Plastic for", prov_names[i], ".jpeg")), width = 10, height = 6 )
}
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis
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2.2 Time plots (Roshni) - adjusted for person*km
total_plastics_time <- plastics %>%
group_by(year, province) %>%
summarise(total = sum(number_collected, na.rm = T),
person = sum(adults_2017_onwards_participant_count, na.rm=T),
totkm = sum(kilometers, na.rm=T)) %>%
mutate(amts_personkm=total/(person*totkm)) %>%
mutate(year_lab = as.character(year))
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
ggplot(total_plastics_time,
aes(x=year_lab,
y=amts_personkm,
group=province,
colour=province)) +
# facet_wrap(province ~.) +
geom_line()+
scale_y_log10()
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 1 row(s) containing missing values (geom_path).
# scale_colour_manual(values=myCol)
ggsave(here("total_plastics_time_adjusted.jpeg"), width = 10, height = 6 )
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 1 row(s) containing missing values (geom_path).
####Grouping by year & Province & type
plastics_group <-plastics %>%
group_by(year, province, plastic_type_new) %>%
summarise(total = sum(number_collected, na.rm = T),
person = sum(adults_2017_onwards_participant_count, na.rm=T),
totkm = sum(kilometers, na.rm=T)) %>%
mutate(amts_personkm=total/(person*totkm)) %>%
mutate(year_lab = as.character(year))
## `summarise()` regrouping output by 'year', 'province' (override with `.groups` argument)
prov_names <- unique(plastics_group$province)
for (i in seq_along(prov_names)) {
chart <- subset(plastics_group, plastics_group$province == prov_names[i])
plot <- ggplot(chart,
aes(x=year_lab,
y=amts_personkm,
group=plastic_type_new,
colour=plastic_type_new)) +
geom_line()+
theme(legend.position = "right") +
labs(title = paste("Total items by Type of Plastic for", prov_names[i]))+
scale_y_log10()
print(plot)
ggsave( here(paste("Total items by Type of Plastic for", prov_names[i], "_adjusted.jpeg")), width = 10, height = 6 )
}
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis
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## Warning: Transformation introduced infinite values in continuous y-axis
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## Warning: Transformation introduced infinite values in continuous y-axis
3.0 Rates
plastics_rates <- gcsc_dat %>%
mutate(amts_person=kilograms/adults_2017_onwards_participant_count) %>%
mutate(ppl_km=adults_2017_onwards_participant_count/kilometers) %>%
mutate(amts_personkm=kilograms/(adults_2017_onwards_participant_count*kilometers)) %>%
mutate(glass_bottle_kg=glass_beverage_bottles/kilograms) %>%
mutate(year_lab = as.character(year))
mean_amts <- plastics_rates %>% group_by(year_lab, province) %>%
summarize(mean_amts_person=mean(amts_person, na.rm = T)
, mean(ppl_km,na.rm = T),mean(glass_bottle_kg,na.rm = T))
## `summarise()` regrouping output by 'year_lab' (override with `.groups` argument)
mean_amts
## # A tibble: 40 x 5
## # Groups: year_lab [3]
## year_lab province mean_amts_person `mean(ppl_km, na.r… `mean(glass_bottle_kg…
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 2017 AB 1.50 21.6 Inf
## 2 2017 BC 3.53 24.5 Inf
## 3 2017 MB 2.73 15.1 0.264
## 4 2017 NB 7.84 14.0 0.176
## 5 2017 NL 4.13 12.2 0.749
## 6 2017 NS 2.58 14.9 0.208
## 7 2017 NT 3.36 3.8 0.5
## 8 2017 NU 7.14 117. NaN
## 9 2017 ON 5.94 Inf Inf
## 10 2017 PE 2.58 25.9 Inf
## # … with 30 more rows
plastics_rates %>%
ggplot(aes(x=province,y=amts_person,col=year_lab)) +
geom_boxplot()+
coord_cartesian(ylim=c(0,20))+
labs(title="Amount per person per province over time")
## Warning: Removed 2115 rows containing non-finite values (stat_boxplot).
ggsave(here("amts_person_time.jpeg"))
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## Warning: Removed 2115 rows containing non-finite values (stat_boxplot).
plastics_rates %>%
ggplot(aes(x=province,y=ppl_km,col=year_lab)) +
geom_boxplot()+
coord_cartesian(ylim=c(0,300))+
labs(title="People per km per province over time")
## Warning: Removed 2345 rows containing non-finite values (stat_boxplot).
ggsave(here("ppl_km_time.jpeg"))
## Saving 7 x 5 in image
## Warning: Removed 2345 rows containing non-finite values (stat_boxplot).
plastics_rates %>%
ggplot(aes(x=province,y=amts_personkm,col=year_lab)) +
geom_boxplot()+
scale_y_log10()+
labs(title="Amount per person*km per province over time")
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 2696 rows containing non-finite values (stat_boxplot).
ggsave(here("amts_ppl_km_time.jpeg"))
## Saving 7 x 5 in image
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 2696 rows containing non-finite values (stat_boxplot).
